AAAI Publications, Twenty-Eighth AAAI Conference on Artificial Intelligence

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HC-Search for Multi-Label Prediction: An Empirical Study
Janardhan Rao Doppa, Jun Yu, Chao Ma, Alan Fern, Prasad Tadepalli

Last modified: 2014-06-21


Multi-label learning concerns learning multiple, overlapping, and correlated classes. In this paper, we adapt a recent structured prediction framework called HC-Search for multi-label prediction problems. One of the main advantages of this framework is that its training is sensitive to the loss function, unlike the other multi-label approaches that either assume a specific loss function or require a manual adaptation to each loss function. We empirically evaluate our instantiation of the HC-Search framework along with many existing multi-label learning algorithms on a variety of benchmarks by employing diverse task loss functions. Our results demonstrate that the performance of existing algorithms tends to be very similar in most cases, and that the HC-Search approach is comparable and often better than all the other algorithms across different loss functions.


Multi-Label Classification; Structured Prediction; Rank Learning; Learning for Search

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